“A Burmese Wonderland”: British World Mining and the Making of Colonial Burma
Bibliographic record
Abstract
This dissertation, entitled “A Burmese Wonderland”: British World Mining and the Making of Colonial Burma, focuses on the Burma Corporation, a transnational mining corporation founded by the future US President Herbert Hoover, whose operations were located in Burma’s remote Northern Shan States. I argue that this company, which by the 1920s had become one of the largest industrial mining enterprises in the world, provides a unique vantage point to explore the complexity of Britain’s Empire during the early twentieth century. Founded and managed by white foreigners from the United States, Canada, and Australia, and primarily staffed by migrant laborers from China and India, my dissertation asks how, over the course of three decades, an international commercial firm like the Burma Corporation was able to build a city on the edge of Britain’s Empire and become an agent of the colonial state. Connected to Britain through a common racial and cultural heritage as well as a commitment to western models of political economy, I argue that foreign commercial agents and experts were crucial to Britain’s colonial project in Burma, taking on the role of colonizer in areas where the state was weak. In doing so, my dissertation brings into question the character of colonial governance and the uniformity of the “British” Empire during this period. It also shows how a supposedly British-operated mine in Burma became a symbol of imperial progressive civilization, obscuring the diverse racial, ethnic, and national actors who made the mine such a success.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".